Summarizing Data

Learning objectives

By the end of the lab, you will be able to …

  • implement basic variable manipulation
  • produce measures of central tendency and variability

Code-along 03

Download and open code-along-03.qmd

Packages

Load the standard packages.

library(here)
library(tidyverse) 
library(haven) # not core tidyverse
library(gssr)
library(gssrdoc)
library(summarytools)

Load your data & codebook

# Get the data all survey years
data(gss_all)

# Load the codebook
data(gss_dict)

Data Management I

Tidying Data

Most tasks related to data analysis are not glorious or fancy.


A lot of time is dedicated to whipping a dataset into the shape needed to be able to analyze it.


This task has different names “data cleaning,” “data management,” “data manipulation,” “data wrangling,” “data transformation.”

dplyr package

The dplyr package provides a complete set of functions that help you solve the most common data manipulation challenges such as:

  • filtering observations based on their values
  • extracting observations based on their values or positions
  • sampling observations based on a specific number or fraction of rows
  • sorting observations based on one or several variables
  • selecting variables based on their names or positions
  • renaming variables
  • adding new variables based on existing ones
  • summarizing observations or variables to a single descriptive measure
  • performing any operation by group

The pipe |>


The pipe operator passes what comes before it into the function that comes after it as the first argument in that function.

sum(1, 2)
[1] 3


1 |> 
  sum(2)
[1] 3

dplyr style

In data transformation pipelines, always use a

  • space before |>
  • line break after |>
  • indent the next line of code

We’ll talk about data visualization pipes later…


Heads Up!

|> (native pipe operator) and %>% (magrittr package) behave identically for simple cases. More info.

function(argument)

Functions are (most often) verbs, followed by what they will be applied to in parentheses:


do_this(to_this)
do_that(to_this, to_that, with_those)


dplyr verbs (functions) will allow you to solve the vast majority of your data manipulation challenges.

dplyr basics

They are organized into four groups based on what they operate on: rows, columns, groups, or tables.


The verbs all have in common:

  1. The first argument is always a data frame.
  2. The subsequent arguments typically describe which columns to operate on using the variable names (without quotes).
  3. The output is always a new data frame.

dplyr grammar

What’s the advantage of dplyr grammar? We can sequence data manipulation!


gss_all |> 
  select(year, sex, agekdbrn) |>
  filter(year == 2022) |>
  drop_na(sex, agekdbrn) |>
  group_by(sex) |>
  summarise(avg = mean(agekdbrn))
# A tibble: 2 × 2
  sex          avg
  <dbl+lbl>  <dbl>
1 1 [male]    26.3
2 2 [female]  23.7

select(), filter(), and drop_na()

Use select() to pick specific columns from your dataset.
Use filter() to keep rows that meet a condition.
Use drop_na() to remove rows with missing (NA) values.


gss_all |>
  select(year, sex, agekdbrn) |>
  filter(year == 2022) |>
  drop_na(sex, agekdbrn) |>
1
Start with the gss_all data frame:
2
Keep only the variables in the dataset that we need.
3
Keep only the respondents from the 2022 survey
4
Remove any observations with missing data for our key variables

group_by() and summarize()

Use group_by() to organize your data into groups based on one or more variables.
Use summarize() to compute statistics like total, mean, or median for each group.

gss_all |>
  select(year, sex, agekdbrn) |>
  filter(year == 2022) |>
  drop_na(sex, agekdbrn) |>
  group_by(sex) |>
  summarise(freq = n())
1
Start with the gss_all data frame:
2
Keep only the variables in the dataset that we need.
3
Keep only the respondents from the 2022 survey
4
Remove any observations with missing data for our key variables
5
Do the next steps separately for each group in the variable
6
Creates a new data frame with one row for each combination of grouping variables 
# A tibble: 2 × 2
  sex         freq
  <dbl+lbl>  <int>
1 1 [male]    1031
2 2 [female]  1363

dplyr() in action

Compare the average and median age at first childbirth for U.S. men and women in 2022.

gss_all |>  
  select(year, sex, agekdbrn) |> 
  filter(year == 2022) |> 
  drop_na(sex, agekdbrn) |> 
  group_by(sex) |>
  summarise(
    freq = n(),
    avg = mean(agekdbrn),
    med = median(agekdbrn)
    ) 
# A tibble: 2 × 4
  sex         freq   avg   med
  <dbl+lbl>  <int> <dbl> <dbl>
1 1 [male]    1031  26.3    25
2 2 [female]  1363  23.7    23

mutate() in action

Use mutate() to add new columns or change existing ones.

What proportion of new parents were teenagers (e.g., under 18 years old)?

gss_all |>
  select(year, agekdbrn) |>
  filter(year == 2022) |>
  drop_na(agekdbrn) |>
  mutate(teen_parent = (agekdbrn < 18) * 1) |>
  summarise(proportion = mean(teen_parent))
1
Manipulate the dataset to variables and observations of interest
2
Remove missing or summarise() will report NA
3
Returns a logical vector (TRUE, FALSE, or NA) and * 1 converts it to numeric
4
Mean of the variable, which equals the proportion of 1s 
# A tibble: 1 × 1
  proportion
       <dbl>
1     0.0773

mutate() in action

Use case_when() inside mutate() to create values based on conditions.

What proportion of new parents had their first child as teenagers, in their 20s, 30s, or after age 40?

gss_all <-  gss_all |>  
  mutate(age_groups = case_when(
    agekdbrn < 18 ~ "<18",
    agekdbrn >= 18 & agekdbrn <= 29 ~ "18–29",
    agekdbrn >= 30 & agekdbrn <= 39 ~ "30–39",
    agekdbrn >= 40 ~ "40+",
    TRUE ~ NA_character_)
  ) 

gss_all |>
  filter(year == 2022) |>
  freq(age_groups, report.nas = FALSE, headings = FALSE)

              Freq        %   % Cum.
----------- ------ -------- --------
        <18    186     7.73     7.73
      18–29   1704    70.82    78.55
      30–39    463    19.24    97.80
        40+     53     2.20   100.00
      Total   2406   100.00   100.00

Assignment operators

Heads Up!

Overwriting datasets and variables can be intentional or unintentional.


Let’s make a tiny data frame to use as an example:

df <- tibble(x = c(1, 2, 3, 4, 5), y = c("a", "a", "b", "c", "c"))
df
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     1 a    
2     2 a    
3     3 b    
4     4 c    
5     5 c

Assignment operators (cont)

Suppose you run the following and then you inspect df.

Will the x variable have values 1, 2, 3, 4, 5 or 2, 4, 6, 8, 10?


df |>
  mutate(x = x * 2)
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     2 a    
2     4 a    
3     6 b    
4     8 c    
5    10 c    
df
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     1 a    
2     2 a    
3     3 b    
4     4 c    
5     5 c

Do something and show me

Assignment operators (cont)

Suppose you run the following and then you inspect df.

Will the x variable have values 1, 2, 3, 4, 5 or 2, 4, 6, 8, 10?


df <- df |>
  mutate(x = x * 2)
df
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     2 a    
2     4 a    
3     6 b    
4     8 c    
5    10 c

Do something, save result, overwriting original

Assignment: Overwriting

Do something, save result, overwriting original

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df <- df |>
  mutate(x = x * 2)
df
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     2 a    
2     4 a    
3     6 b    
4     8 c    
5    10 c

Do something, save result, not overwriting original

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df_new <- df |>
  mutate(x = x * 2)
df_new
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     2 a    
2     4 a    
3     6 b    
4     8 c    
5    10 c

Assignment: Overwriting (cont)

Do something, save result, overwriting original when you shouldn’t

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df <- df |>
  group_by(y) |>
  summarize(mean_x = mean(x))
df
# A tibble: 3 × 2
  y     mean_x
  <chr>  <dbl>
1 a        1.5
2 b        3  
3 c        4.5

Do something, save result, not overwriting original when you shouldn’t

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df_summary <- df |>
  group_by(y) |>
  summarize(mean_x = mean(x))
df_summary
# A tibble: 3 × 2
  y     mean_x
  <chr>  <dbl>
1 a        1.5
2 b        3  
3 c        4.5

Assignment: Overwriting (cont)

Do something, save result, overwriting original
data frame

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df <- df |>
  mutate(z = x + 2)
df
# A tibble: 5 × 3
      x y         z
  <dbl> <chr> <dbl>
1     1 a         3
2     2 a         4
3     3 b         5
4     4 c         6
5     5 c         7

Do something, save result, overwriting original
column

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df <- df |>
  mutate(x = x + 2)
df
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     3 a    
2     4 a    
3     5 b    
4     6 c    
5     7 c

Assignment: Overwriting (cont)

Do something, save result, not overwriting original.

gss_all <-  gss_all |>  
  mutate(age_groups = case_when(
    agekdbrn < 18 ~ "<18",
    agekdbrn >= 18 & agekdbrn <= 29 ~ "18–29",
    agekdbrn >= 30 & agekdbrn <= 39 ~ "30–39",
    agekdbrn >= 40 ~ "40+",
    TRUE ~ NA_character_)
  ) 

gss_all |>
  filter(year == 2022) |>
  freq(age_groups, report.nas = FALSE, headings = FALSE)

Do something and show me

gss_all |>  
  select(year, agekdbrn) |> 
  filter(year == 2022) |> 
  drop_na(agekdbrn)  |>
  mutate(age_groups = case_when(
    agekdbrn < 18 ~ "<18",
    agekdbrn >= 18 & agekdbrn <= 29 ~ "18–29",
    agekdbrn >= 30 & agekdbrn <= 39 ~ "30–39",
    agekdbrn >= 40 ~ "40+",
    TRUE ~ NA_character_)) |>
  group_by(age_groups) |>  
  summarise(
    count = n(),
    proportion = round(count / sum(count), 3)
  )

Summary Statistics

Median & Mode

Let’s use dplyr grammar to find the median and mode for the childs variable.

gss_all$childs <- zap_missing(gss_all$childs) 
gss_all$childs <- as_factor(gss_all$childs)   
gss_all$childs <- droplevels(gss_all$childs)  

gss_all |>
  filter(year == 2024) |>
  freq(childs, report.nas = FALSE) |>
  tb()
1
Use dplyr grammar, starting with the name of the df and a pipe
2
Use the freq() function as usual
3
Add the tb() function to turn the table into a tibble

Median & Mode

# A tibble: 9 × 4
  childs     freq    pct pct_cum
  <fct>     <dbl>  <dbl>   <dbl>
1 0          1029 31.4      31.4
2 1           484 14.8      46.2
3 2           851 26.0      72.1
4 3           475 14.5      86.6
5 4           243  7.41     94.0
6 5            96  2.93     96.9
7 6            53  1.62     98.6
8 7            16  0.488    99.1
9 8 or more    31  0.946   100

Median & Mean

median(gss_all$hrs1, na.rm=TRUE)
mean(gss_all$hrs1, na.rm=TRUE)

# show me summary statistics
summary(gss_all$hrs1)
1
na.rm is a logical evaluating to TRUE or FALSE indicating whether NA values should be stripped before the computation proceeds. 
[1] 40
[1] 41.11279
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   37.00   40.00   41.11   48.00   89.00   32371

summarize()


gss_all |>  
  select(year, hrs1, sex) |> 
  filter(year == 2024) |> 
  drop_na(hrs1, sex)  |>
  group_by(as_factor(sex)) |>  
  summarise(
    count = n(),
    mean = round(mean(hrs1), digits = 2), 
    median = median(hrs1)
    ) 
# A tibble: 2 × 4
  `as_factor(sex)` count  mean median
  <fct>            <int> <dbl>  <dbl>
1 male               869  41.7     40
2 female             891  37.3     40

descr()

Univariate statistics for numerical data


# all years
descr(gss_all$hrs1)
Descriptive Statistics  
gss_all$hrs1  
Label: Number of hours worked last week  
N: 75699  

                        hrs1
----------------- ----------
             Mean      41.11
          Std.Dev      14.12
              Min       0.00
               Q1      37.00
           Median      40.00
               Q3      48.00
              Max      89.00
              MAD       7.41
              IQR      11.00
               CV       0.34
         Skewness       0.18
      SE.Skewness       0.01
         Kurtosis       1.42
          N.Valid   43328.00
                N   75699.00
        Pct.Valid      57.24

descr()

gss_all |> 
  filter(year == 2024) |>
  group_by(as_factor(sex)) |>
  drop_na(hrs1, sex)  |>
  descr(hrs1,
        stats = "common") |>
  tb()
1
Which stats to produce. Either “all” (default), “fivenum”, “common” (see Details), or a selection. See ?descr
2
Don’t forget to make a tidy dataset out of freq() or descr() outputs 
Error in if (grepl("^get\\(", deparse(call[[.p$var]]))) {: the condition has length > 1
# A tibble: 2 × 10
  `as_factor(sex)` variable  mean    sd   min   med   max n.valid     n
  <fct>            <chr>    <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl> <dbl>
1 male             V1        41.7  13.7     0    40    89     869   869
2 female           V1        37.3  13.7     0    40    89     891   891
# ℹ 1 more variable: pct.valid <dbl>

Variable Descriptions

Let’s familiarize ourselves with the premarsx and polviews variables.


In the console, type ?premarsx and hit enter. The Help pane will show you the question text, response options and values.


Now, do the same for polviews.

Change variables

gss_all <- gss_all |>
 mutate(pol3cat = case_when(
   polviews >= 1 & polviews <= 3 ~ "Liberal",
   polviews == 4 ~ "Moderate",
   polviews >= 5 & polviews <= 7 ~ "Conservative",
   TRUE ~ NA_character_),
  pol3cat = factor(pol3cat,
                 levels = c("Liberal", "Moderate", "Conservative"))
  )
1
Save over the dataset with an added variable.
2
Creates a new variable by assigning labels based on values of polviews
3
Set everything else to “missing”
4
Convert the variable to factor with specified level order